{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voting Classifiers\n",
    "\n",
    "* Good classifiers can be built by aggregating predictions of various *weaker* classifiers, and returning the class that gets the most votes. (A \"hard voting\" classifier.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "heads_proba = 0.51\n",
    "coin_tosses = (rnd.rand(10000, 10) < heads_proba).astype(np.int32)\n",
    "cumulative_heads_ratio = np.cumsum(\n",
    "    coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)\n",
    "#cumulative_heads_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NxGwEPVr5oifvti0AvJd/kNsmG1tNDyyrgqdWkz5nPM4mlqJxU722o/7R16yf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\nzMza+H+QDz0zi2bafwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa49738b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3.5))\n",
    "plt.plot(cumulative_heads_ratio)\n",
    "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
    "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
    "plt.xlabel(\"Number of coin tosses\")\n",
    "plt.ylabel(\"Heads ratio\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.title(\"The law of large numbers:\")\n",
    "plt.axis([0, 10000, 0.42, 0.58])\n",
    "#save_fig(\"law_of_large_numbers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression 0.864\n",
      "RandomForestClassifier 0.872\n",
      "SVC 0.888\n",
      "VotingClassifier 0.912\n"
     ]
    }
   ],
   "source": [
    "# build a voting classifier in Scikit using three weaker classifiers\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "# use moons dataset\n",
    "X, y = make_moons(\n",
    "    n_samples=500, \n",
    "    noise=0.30, \n",
    "    random_state=42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, random_state=42)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "log_clf = LogisticRegression(random_state=42)\n",
    "rnd_clf = RandomForestClassifier(random_state=42)\n",
    "svm_clf = SVC(probability=True, random_state=42)\n",
    "\n",
    "# voting classifier = logistic + random forest + SVC\n",
    "\n",
    "voting_clf = VotingClassifier(\n",
    "        estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
    "        voting='soft'\n",
    "    )\n",
    "voting_clf.fit(X_train, y_train)\n",
    "\n",
    "# let's see how each individual classifier did:\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))\n",
    "    \n",
    "# voting classifier did better than 3 individual ones!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If all classifiers can estimate class probabilities (they have a predict_proba() method), use Scikit to predict highest class probability, averaged over all individual classifiers. (*soft voting*)\n",
    "\n",
    "* Often better than hard voting because it gives more weight to highly confident votes. Replace voting=\"hard\" with \"soft\" & ensure all classifiers can estimate class probabilities. (SVC cannot by default -set probability param to True.)\n",
    "\n",
    "* This tells SVC to use cross-validation to estimate class probabilities. Slows training times & adds a predict_proba() method)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging & Pasting\n",
    "\n",
    "* Another approach: use same training algorithm, but apply it to different subsets of the training dataset.\n",
    "* **bagging**: sampling the dataset **with** replacement.\n",
    "* **pasting**: sampling the dataset **without** replacement.\n",
    "* Final prediction = based on an aggregation function.\n",
    "* Predictions can be made in parallel -- good scaling properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Train ensemble of 500 Decision Tree classifiers\n",
    "# each using 100 training instances - randomly sampled from training set\n",
    "# with replacement.\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "        DecisionTreeClassifier(random_state=42), \n",
    "    n_estimators=500,\n",
    "    max_samples=100, \n",
    "    bootstrap=True, # set to False for pasting instead of bagging.\n",
    "    n_jobs=-1, \n",
    "    random_state=42)\n",
    "\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.856\n"
     ]
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf.fit(X_train, y_train)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n",
    "    if contour:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k4PVu6ph2BgJb8ufpame9e7uapq9bdyup1AHi8VdIp4/R09M4gwss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\nqiAIgiAIguA5/j8s+egymsv++QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa485fd69b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.title(\"Decision Tree\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_decision_boundary(bag_clf, X, y)\n",
    "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
    "#save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out of Bag Evaluation\n",
    "* Bagging: some instances may be sampled multiple times - others not at all. On avg, ~63% of training samples are used. Remainder 37% = \"out of bag\".\n",
    "* use *oob_score=True* in Scikit to do automatic oob evaluation after training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89866666666666661"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oob_score_: predicts classifier results on test set.\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(),\n",
    "    n_estimators=500,\n",
    "    bootstrap=True,\n",
    "    n_jobs=-1,\n",
    "    oob_score=True\n",
    ")\n",
    "bag_clf.fit(X_train, y_train)\n",
    "bag_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90400000000000003"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# did oob_score_ do a good job?\n",
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = bag_clf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.36363636,  0.63636364],\n",
       "       [ 0.38586957,  0.61413043],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.        ,  1.        ],\n",
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       "       [ 0.98795181,  0.01204819],\n",
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       "       [ 0.        ,  1.        ],\n",
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       "       [ 0.97311828,  0.02688172],\n",
       "       [ 0.31460674,  0.68539326],\n",
       "       [ 0.98870056,  0.01129944],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.00581395,  0.99418605],\n",
       "       [ 0.99009901,  0.00990099],\n",
       "       [ 0.        ,  1.        ],\n",
       "       [ 0.0304878 ,  0.9695122 ],\n",
       "       [ 0.97849462,  0.02150538],\n",
       "       [ 1.        ,  0.        ],\n",
       "       [ 0.03508772,  0.96491228],\n",
       "       [ 0.64864865,  0.35135135]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oob decision functionfor each training instance\n",
    "bag_clf.oob_decision_function_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Patches - Random Subspaces\n",
    "\n",
    "* **BaggingClassifier** supports feature sampling. Params: *max_features* and *bootstrap*. \n",
    "* Very useful when handling high-dimensional datasets.\n",
    "* \"Random patches\": sampling features & sampling instances.\n",
    "* \"Random subspaces\": sampling features & keeping all instances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forests\n",
    "* RF = ensemble of Decision Trees\n",
    "* Typically trained via bagging\n",
    "* **RandomForestClassifier**: designed for DT classification\n",
    "* **RandomForestRegressor**: designed for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train an RF classifier with 500 trees limited to 16 max nodes each.\n",
    "# splitter=\"random\": tells RF to search for best feature among\n",
    "# a random subset of features.\n",
    "\n",
    "bag_clf = BaggingClassifier(\n",
    "    DecisionTreeClassifier(\n",
    "        splitter=\"random\", \n",
    "        max_leaf_nodes=16, \n",
    "        random_state=42),\n",
    "    \n",
    "    n_estimators=500, \n",
    "    max_samples=1.0, \n",
    "    bootstrap=True,\n",
    "    n_jobs=-1,\n",
    "    random_state=42)\n",
    "\n",
    "bag_clf.fit(X_train, y_train)\n",
    "y_pred = bag_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rnd_clf = RandomForestClassifier(\n",
    "    n_estimators=500, \n",
    "    max_leaf_nodes=16, \n",
    "    n_jobs=-1, \n",
    "    random_state=42)\n",
    "\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "y_pred_rf = rnd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97599999999999998"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# almost identical predictions\n",
    "np.sum(y_pred == y_pred_rf) / len(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance\n",
    "* important features likely to appear closer to root of tree\n",
    "* unimportant features likely to appear closer to leaves - if at all.\n",
    "* Scikit finds avg depth of feature appearance across all trees in an RF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm) = 0.112492250999\n",
      "sepal width (cm) = 0.0231192882825\n",
      "petal length (cm) = 0.441030464364\n",
      "petal width (cm) = 0.423357996355\n"
     ]
    }
   ],
   "source": [
    "# rank features by importance in iris\n",
    "# #1: petal length: 44%\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "\n",
    "rnd_clf = RandomForestClassifier(\n",
    "    n_estimators=500, \n",
    "    n_jobs=-1, \n",
    "    random_state=42)\n",
    "\n",
    "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n",
    "\n",
    "for name, importance in zip(\n",
    "    iris[\"feature_names\"], \n",
    "    rnd_clf.feature_importances_):\n",
    "        print(name, \"=\", importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.11249225,  0.02311929,  0.44103046,  0.423358  ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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95j/b3R92nkspSEwzz1e+8pPzJqMnbDuObWfanODbQGeY5MrKG0FI5WbLccfZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa493d0be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "for i in range(15):\n",
    "    tree_clf = DecisionTreeClassifier(\n",
    "        max_leaf_nodes=16, \n",
    "        random_state=42+i)\n",
    "    \n",
    "    indices_with_replacement = rnd.randint(\n",
    "        0, \n",
    "        len(X_train), \n",
    "        len(X_train))\n",
    "    \n",
    "    tree_clf.fit(\n",
    "        X[indices_with_replacement], \n",
    "        y[indices_with_replacement])\n",
    "    \n",
    "    plot_decision_boundary(\n",
    "        tree_clf, X, y, \n",
    "        axes=[-1.5, 2.5, -1, 1.5], \n",
    "        alpha=0.02, \n",
    "        contour=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting - AdaBoost\n",
    "* One strategy: pay more attention to training instances that predecessor underfitted - forces new predictors to concentrate more on the \"hard cases\".\n",
    "* **Disadvantage**: results depend on previous classifier (sequential), so algo cannot be parallelized. Not great for scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1LR+7+WqMfFImYM6j9H+a2aGQds4n3QStnQsVyxytbybw5hOHeHm3goYW/AGD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\nQya8k19EEEOorx3nOSoYGVtSU1q0dk5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TBWiJNHf8+H2ioQGkQIqq8hK+deNGKkJubUVs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EavfbF/n4lBe5vA3NgMs2XsaW+zan7XcuUKj+oe1NvoypAJMdp1DpA7kmcU6Jvq5H9yzBWz6W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\nRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa485ee7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries of five predictors on moons dataset\n",
    "\n",
    "m = len(X_train)\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
    "    sample_weights = np.ones(m)\n",
    "    for i in range(5):\n",
    "        plt.subplot(subplot)\n",
    "        \n",
    "        svm_clf = SVC(\n",
    "            kernel=\"rbf\", \n",
    "            C=0.05)\n",
    "        \n",
    "        svm_clf.fit(\n",
    "            X_train, y_train, \n",
    "            sample_weight=sample_weights)\n",
    "        \n",
    "        y_pred = svm_clf.predict(\n",
    "            X_train)\n",
    "        \n",
    "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
    "        \n",
    "        plot_decision_boundary(\n",
    "            svm_clf, \n",
    "            X, y, \n",
    "            alpha=0.2)\n",
    "        \n",
    "        plt.title(\"learning_rate = {}\".format(learning_rate - 1), \n",
    "                  fontsize=16)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
    "plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
    "plt.text(-0.5,  0.10, \"3\", fontsize=14)\n",
    "plt.text(-0.4,  0.55, \"4\", fontsize=14)\n",
    "plt.text(-0.3,  0.90, \"5\", fontsize=14)\n",
    "#save_fig(\"boosting_plot\")\n",
    "plt.show()\n",
    "\n",
    "# left: 1st clf gets many wrong, so 2nd clf gets boosted values.\n",
    "# right: same sequence, but learning rate cut in half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y8avLcotHdR6LpvUDoGkhcrkfA29vOObg4L28//31o8HC4asLdjM+AV3fQjT6\nBrLZYpVlRRlYc/NmtzYNpjmDpm2t+UxVI5jmDH19d9Ts7E3zMsnkk6W+Ms2bunq9VfFq0gul23sJ\nT5BQXIwuXfoaqtoPyFKF2TyRyC2rWnBurV/EeqaBXO4spnm+sktdL/9QuTx+Nc3ksTSzuLTiE9D1\nLZWIr3RaJRxOdOkbLqXZ37+d58QwRuuagQxjdMnOPpM5ipSSUOhwpdoCLG/q2gitilcTz/leiydI\nKD6cfX13kcm8VGmcFA4fRlH0yq6u+mUWQgcEUpptC4D1eBHrmQay2ZcJBveva+nviQmH48d31Pgn\nAEZHLzZl/15pcVmL8ub1kiILhTiDgy/xm7/52SXPSbO/f7vPyejoeyo+kfJv7ThpJiY+uET4SmkR\ni91eEzK9khN7M5aM97SM7WMrH1MfT5CUGBu7r27C3OIWs6CRSDyBlJJY7Pa2BUD1i2ial8nlTlMo\nzHHmzL9h9+7fX5WXsd7u3e+fIBDYWXNct0NOTTNPKtV4B/dbv3WZXG6ShYUvoChRFCWM66Zx3SSK\nch9zc5c7un48fhqfbyuWdbWNrpQKicRzZDIfYWHhVwiFsuj6LjRtEADD8FOsTNwci5MiLWuWZPIZ\npqa21RUAzS7E7S7Yw8NFc2B11NbExAcrn1cL38nJhygUippX9bOo60MNfYTdiqByHId4fAEpZUvn\nLSYYDBEMhjoao10tY/OEDeteZnunLGciqW4xm04fq5hg8vkzxGJ3AK3vxMovomleLvVIL5Ymsay5\nVdVMFu/ey4vIakTCSCk5cuRpvvSlvyWVWrk+aCwWZXz8NKFQmkwmzNTU9SQSPwJ+1NE8Dh6cQdfP\nUihcLX4YDi/Q17fApUtXKBRu5ZvffDOW5ceyJK6roqoWw8MKDz4YbqrMymKu+nz0uuaiZhfiThbs\n4eG3VwTHcpQ1VcuaL1VZFoCKpo00fBa7EUE1PX2O//pfv8LkpI2Uounz6hEMSn7pl97I7bff3dE4\n7eCFDXuCpIZGJpLql9lxkihKBCGohJi2sxMrv4i53OmKk9l18+j6UKUacC/H9K9EOp3iS1/6Aj/8\nUY5sXwJi5ornXAJembqu6hMHYhc7mgeAnejjlutOYhd0TFvD8BUIDVzmYjpKQrW5+52fYDY5yNCW\n89iuyoWFYbA0jILBD34wSj4fw+/3r3gdy5qtOOot6zK6XpvHUv2cNLsQr1WhxfHx93HmzL9BShtd\nHyIQKFaqLDlWAAAgAElEQVQHsO1E3Wexk+fGcRweffRbfOMvjzMrchBNgehMI8HS+NOHn+XZZ48Q\njWoND9s82kNv4QmSJqh+mcu9yqWEcq/udl7s8otYKMzh8w3gunlcN084fHhNu8qFwwcYGLhnSeJa\np0Ls8ce/z7PPpsj4TZS+NH6/H0XpbNfZLnm2cHzex3UD5+kPZkhbITJ2hKwbQ/MX5yRUgVRUAloB\nnyGw1Tz5goY5m+MnPznFbbe9GWjs+C4U4pXQYVWNIMR8qbfLVb9D9XPS7EK80nHdCtgIhw/g908Q\njb4JIa5qj42exZWCHJab12uvneIHPzjCbCaA2LmA4ddQfe0vRVJK8rkceTvOkSMD3HTTAlDfzOVp\nD6uDd/eaoPplDgR2k0w+WYpyOVjTq3wlFr9cAwP3YJoXsKyiPTocPlzZBa5VklU6fZyFhUcJhQ4S\njRbLvi8sPEowuLsjYWJZJlKqKLrE5/Pxj37x1zm8v7mOhmvB/OTHcQsJ1NJO/4ffGGV0DIRi4O8f\n59ip4yBAyqKPB5Z3fJtmpCZ8XNeHyefPY9tXkNJdIgCaDWVd7rhuB2y0qv000uBXmpdtF3CcovBW\nfAq3ve4N/Mr/8Sstz7fM2amzfPKLf4blA9dVO/a3eLSOJ0iaoPpldpwUsdjtlKO2NG1rU7Hs9V6u\nXO5RRkffw8LCoxUnfyuCafH47exM23Xmtny99VFGGhIefBvxqYcBUNQI0rWQrokRPoTI5eues9y9\nGhq6mbNn+5AyixAaqjrEiROvx3VzfOhDMVQ1iGGMo2l9lfL2zYayNjqu25FT3TJztjyvDp8N0WsP\n14bF8nq2rzadxq83erlyuZMdJ1l1sjNtx5m73rWaGtGK/dsI30Df+P2k57+Nk59GKDr+6C2o+hBQ\n/7vXu1f5/DSXLn2NX/u1DOBD17ehacXikR/96CfZudOmr++OqjPql0dph27XnupWwt9q18TqNTZP\n2PD56XbP9ATJGrHcy9VMccXldvyd7Ezbceb2ag5Bq/ZvI3wDRvgGAK47cFUIXZ4Pko1vQWYCDMeu\nVI5ffK/S6ZeJxx8DbBQlguOkMc1zCDGBoujYdoJg8HVd/pZX6dQR304TsbWY10bDc9KvsyARQvw8\n8CcUA/Y/I6X82KJ/vxv4S+C10kd/IaX8/9Z0kl2ilZer1R1/JzvAdswZvbDjNNMnKtqE6h8jPPg2\n4M62x6teDJ54/hm+8q2v0pfycfN1Z5DSz+TkEQKBfeRyjwLF75tKPQuIUm2uQCWSL59/DV0fQVGM\nlvuitEInpqjV1Cp7uSbW5tEeeot1EyRCCBX4U+BnKdoSnhFCfFNKeXzRoY9JKf/+mk+wy7TycrW6\n4+9kB9iOOWO9d5xm+gTxqYdRfFFUfQS3kCA+9TBO4SAQbmmseuawy/NvIJU3+Y13fRSfYwDbKj6t\ngYF7yOVOljLCTfz+CWw7juvmcJxcqeqBU6pAnODy5a+j61vbara1Ep2YolZTq+ylmliL8bSH1WE9\nNZLbgNNSylcBhBBfBt4BLBYkm4J6L1c0ehvz849w4cLDNaaFVOootp2olGsJBPagaYMNd/z1hFQ+\nP4nrjnDy5AcbmsYWmza2bbu/qZd9vXec6flvF4VIafEr/2mbF4B9LY1V1xxmZEkfC2NaBtI1AFHj\n0yq3vX3xxV8qCdQB8vnTAEjpIoTAtucQQsN1MzXNtrotTP7Df7iNc+duX/J5o3715d/80qX/ha6P\nEAjsqRTsbLZLYTMm17KJrHx8+RkPBPaRTj/GTTc9zfB8P6/akTpXubbYDLkt6ylItgHnq/4+Bbyx\nznF3CCF+AlwAHpBSHqs3mBDifuB+gImJbV2eaneotj83Mi0MDNxDPn+O4uIVxXGKi1AwuJ9QaFfD\ncauFlBAGUkoUxUBVh+qaLToxbaz3jtPJT6MuSvZT1Aiuk+3aNTTVwbR09Ko3ZPFCW65npaphpPQB\nBYRwUJQIur6FsbEEFy4MYRijuK7FxYtzhMMjTEw0V96+GVrpV1/7m49g28mSee5WDGN4Ra2y1Wdm\n8fGZzKtcvPhVpNxNPh/CMPLcPDqDVG7s6B5sdDrJbekVIdTrzvbngQkpZVoIcS/wDWBPvQOllA8D\nDwPceutNPR9I3si0MDPzOYLB/WSzL+O6JkIYgEku9zITEx9oON7i2kmKoi9rtujUtLGeVVhV/1hN\nDgiA66QY2252zf5dcFQM3QLXqHy2eKGtrmdlWdMIESUSuRXTPIuqRnjve/8URfHT13cHUrpY1jT7\n9v1xy3PpFtW/eTC4l2TyGUCQy51CVY0VtcpWn5nFx1vWRVQ1jGXNAxqm5UfYGsPqS13/rtcKvZJg\nuZ6C5AKwverv46XPKkgpk1X//4gQ4s+EEENSyrk1muOq0chhbZozRKNvQlXD5HKncZwkPl+0VJG4\nuYW7GWf4WjrMj37vV/j4CzfSH+uv+bzdXdPiHBDXSeHaSf7VRwVGeKErc06ZQQzdxOcAyIb5PeV6\nVuXdt+s6FArxSufGaLSoZPdC1FL1b36198orWNZFNO3OFbXKVp+Zxcc7TrFVgJSzQPFZMG0NXVyp\ne343OPq3v8K/P3IDsUis5vONZDbaCKynIHkG2COEuI6iAPlV4NeqDxBCjACXpJRSCHEboADzaz7T\nVaCRw9owRkt/Dlds1+Ue6p2OXb2QraXDPJsYYuuhHMNDtfbwdndNi3NAVP8Y0ZFfrYTydoNLU3v5\nxMf/MwPhFP39KqHQEIYxzu7dkbq+h3KpmfPnP0ExCNGHqkbJ519FUXQURV33qKXFv7mubym1Sriz\n4vdp5XxY/plZfLyqRrHtBEJcDYgwfAUs2V/3/G6QTQwxclOewf7akileSZTusm53U0ppCyE+AHyX\n4pv3WSnlMSHEe0v//mngl4H3CSFsIAf8quyg/sFaN5Ja7rqNHNblTPfFnzeqrVQMST3Z1NjVC1kg\nsI+5uU/gujaaNoiuj/bEYtcs1TkgnVAvHPTyfBCkQNUzuK7CyMg2duwo+qeWSybM5U4Si92Jzxcr\ntQ4+hWXNUShcbKk1wGo9p50GSQQC+5id/QRSNvfMLL6ero9gmhdQlN1AAkPPY/gKxJ3WSucs9guk\nMzonzrwXQ49z297HWxrLozusq1iWUj4CPLLos09X/f8ngU9241rrlY293HUbOayDwd1N1VYqOy8j\nkVsIBHY2NXZ5TgsLjxII7MeyZjDNaXK50wQC1zM/X/w5eiFUcy2oZ9544vln+Je/0Vx0VfWin04f\nJRy+GZ8vVum0WPaNtCJEWnlOJybqZ8rXc+h3EiRRfmaCweIzUyjM4zgJtm//7YbnL75eKLSLoaG3\nce7cY/j901yZ7+fE+b3sj7TWT2mxXyCRNDm3MEvq4sgyZ/Uuzea21HOsP/0jnXOvqdzx1pWra68m\n14x+t2Y1pVq47o4dDywbOlk9h8nJh5ib+w5C6IRCh/D5YhQKZeflRYLBXU2NvXhOPl8Ey7qElJDN\nnsZ1TZLJF7nuut+9ZoRJuyxe9IV4hUTiCfr67qyE+bZqLmz1OW21V0q7QRK1jvqidmbbCXK5k8DS\nnifLhZYvLOzkxRdDvDYXRLn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nYGALQgikdCuLdbl1r88XwucrJt5JKdH1FDffvI8Hfvut\nlfEjkQjXXbcHRamNnKveiJjmZbLZlwGBbSeW9au14/vIZk+VhEixbpgQfoTQmZ//OvBz7d+8LtLK\nZiObFly/vxh1df6sDytfXOayWcGlaZV0UiESc2siFtNJUclEj8QaVzlYTZr1KxYFqtm2L3tz6lmb\niGpTRD5/DsexKv0goPhCl3eT3QwRXins2HGyaFqIXE5FCIkEJArSzZON/xDcAvOTHyc8+LautsBd\nb6pfzEQqQObKFty8j/5Q477ji3e+x07/KplUloDjcPid/5PDh2/BNE8uu1j7/eOMjl5iauqqlue6\nFooywIEDPl73ultXnHv1RiSXO11a6I0abbaeX62ZXKbaBl8fwu8/VzVHA59vACH6uh7B9dCDUV45\nsYcTZ96La6r48n5OnChQKKTY9ZbVSV2z8gLDX9SsLIurEX9xhZ/7haK2+t1vBAAqf18v1mqT5AmS\nHmaxKcJxLFKp5wAIBHYu0hJaixxrxla+3JiqGqScN6EbGVwhEVhI10TaaYzobbiFBPGph+kbv7/r\nwmS9ksOqx375zMv8+Zf+C4XLEfoy0YbnLN75TsWvINUkmaqkw5UW68HBe3n/+4vPQvW/FzWI5hzY\n1ZqF4yRRlAiua+LzRSvXbWSuWqnjZvVzatspLGsWRQmiqiGktLGsaRwnht8/Tq6La+v0OR+j24sd\nEp2shu4LMjBg8swzN3Plb96D4reJH9lDLFIUlO0+H8GQrGgXlgVlw4umNz7nWsITJD3MYp9IWRMp\ndkbU29Y8uhEubBjjSHkcKRWef/6t2I4fn+KSN/v4gz/4LIrqZ2TsCu/9ra+Qnv/2ioKkVcGwVvHx\nq8Xs2YPkFob5xjfezbFj2wgG91Io7GNo6Cjvf//nlvy2yy3mruti21fvhaqq5HInl2wUFgsr204C\nknC42DN9OXPVcpuKxc/p1q1nmZrai5QWqhpCCBXXLbB160uY5izJ5EPs3TtP0jlAYz2uOY4e0SgU\nBphbuBXpKCi2j7k5h0ymj63RedSQxcj4dgb7i+bAdp+PQzcXOPeauiTRtmAVKyMsjuaKxNy2Oxdu\nxAz6jfHWXaOUe5Gk08cqBRsDgd2o6gT79v1x2+N2Gi4spSSTcZmZCaPqFnkrhBFIEwwo+HSV8R1Z\nIMv01ACKGsHJT684ZrcFw1rknLSLrusUzAC+QBqbBNPTz1Es+AnHjg3yD//hP+COO+5e4uOot5if\nOfMyX/nK17ly5er3Ghpa4M1vnmHr1uuXbBTKwqhYvDFBILAfTRvEthNt+9UW++7+6T/9OIoSxrbn\n0PVhbDuJlAq2PY+i3IsQQ+j6NK8/9CxHrnSWY5JNCwa2OCRyWWRBQVU0AgEH113qFO+UciTW7EW1\nYtoCUdJQaumkc+FG3CT17sw8EEInkXgCVY2UzBB5ksknicVu72jcTjKQM5k0X/3qf+PVs7eTMIdB\nHyCeHqBfG8Zn5peYWVwnhepf2nBotenVnRvAjXtv5OTTBXL5HG4swZXC1SS3bMHmk3/+NM8+e4Rf\n//VfZ8uW+sU58/kc3/jG1/juX08T92cQxtVqAkPRE7zwExjZcpJDh27E77+6Udix44Eak1Q3/GqL\nnfHlzp2aNkwsVsymWVj4HoaxFZ8vhhB5CgUD03bYveVCy9drhKbaBI0sfn8BVd1G0MhiLrPELd5s\nHH1BI5sRBMOyEoVVPm5swubpH+mAUmPa0v1F/2AyrpBJi5rxemHT0izFuRptG+o8QdJj1DotT+A4\nJqoaQYhiP5JK1dcmx6jn/+gkA/nb3/4KP/xhit1v+QqiL8GWLUNMP/5R9uzXcKwEP/prh+999wBC\nKExP9/H8Mx9FUaOEIlql1lAvq+hrgc/nY6AvQt7M4/cbWGrVgqMXsLZe5qmndmAYn+N97/twzbnl\n3/bUqR8zO+uiDm9D0QQ+zUf5uYj2x0klo5w/76JpP+Gmm95Yd6PQTkWGes9WtcnMcfJY1hVM8zyG\nMY5pXkJV/dj2ArHYW2rGMi2DaH+cxoHSzaMpNiEjB45a0kYko4OXuVSng2eZ5fKJqilrFmVNoV6y\n4cHXFdrqeljPhHX0BW3Nq2c/8JEk//H3z5xt93xPkPQQi30Xrvs8QqhI6ZQia8q9SBoXP2zG/9FJ\nReFsNoPjaAjdxR8wePc77+MTzwcAG1UfImcKorHLSNdE0M+2CQOhqiTjtXWHriXqmdkyacHINp3b\n33Q3BbsoYB3X4a//9hTCJ3EcFdOs/Z2rf1vLCqJpV7j1hpd4aX4XP/+zv8me6/YQT8R54vvHMQwL\nJ61gl8duI69osdAIBPaxsPBo3WdrfPx9TE9/gUTiMXy+Afr67sKyZkgkHqOv7y76+u5CUWo3vIZu\nkjKDaA2u3wzBkCSVVKGgk7E0cHxomouiuNiOSn+wuZpnrZRAWVxLbjkT1kMPRnn0LwNkM7Wbv2C4\naFUW7NgAACAASURBVBr7+UVRXY9/3+Dca75K1Ndy82iV1fS9XFtvdI+z2HehaUPYdgKfL1QxEazU\ni6QZ/0c3KwovtuMLxY+vtGAJRUOo3bdVQ2/7QBZT7yX9nfcMcO41lUe/eTUDXkrJ5Qt7Of7YL3N4\n9IUl59T+tgLL8iM1k12DF4iGo0RCEWzb5uzCdm4YPIGt5wGlLf9HvQ3J7OwnCAb31322dux4AMPY\nwsDAz1ZpujeUntdYzeZFShdNMzEUhxOz4+zvoHTboZsLRAYWuHT+u5h5A7WgEQ47PPHE3czNbkcz\nLITiJ5coPiuNno/VamU7fc6HEDA6XjtG2RS2mFRCwedbnZa6q+l78QRJD7HYdxEIXE8q9SyWNYeU\nblOaQ7P+j24Wmqxe1IsvR1G4tBIa2apg2OimsbLNPRSuzfQOxq6QSw6VE+lrqPfbmgWdWCRe89mV\nXD/Pv3YD+/xJfL4FNC3W8kah3oakGMY7U5PHVP1sLffsVW9epDyHZRk8f+IAV/o6M2yNTdi8csJg\n9uIOhCuhYJDLORw+/CN+/pf/iIImuOXNf8LeXXs7uk75WmuxedF0ueotdReXAir6hHoks92jMxb7\nLgxjGMfZT6FwEcuabkpzWMsKrD/5m1/m3724m2Cg9jGKxFzueKu5RD1fjo0sGAaj8xy6/mhth8T6\n9UwrPPCR5JIdouM4/PCpE1y5OFD3nHq/raFZpPNLW8zOp/p57tjrCIWyvOMdD9QdbzlfWj2hoGmD\nFArzNZ8tTppc7tkrb15yuZd45ZW/YP5KEKXvfMN71AwPfCTJ5NQk//1rn+bw0GncZIyBAYjHJWgW\nJ+d2cUtHV6i91lowPOIuSWTsdhuG2lpgSuk59DLbNwX1fBeKorJ79+83vZtcy46K2fggo683iYSv\nqh7VrWuvBQaCV7hx4lXIhlvukNgq1b9tudIwmsUr87tYOa+9lpV8afWEgq6P4jgJbDtR99n61Kfe\nzyuvnEUIHSG0SvXfvXt38rGPdeceNGIh28/zpw9xoG8WXZ/DsgY5OrWDpN9Y+eQS5QrQ1XRbE9is\neIKkh+iG72K9OypWJ2JJYGaq6CMJhmTFLNDui9mLiVq7hi5g2hqYAaQ0MU0Dxe68Q6KLpFBwmZqa\nrPo0hKa9g0zmB9j2LLl8mBMXt5PSmtf8yqzkS2u0qdm+/bfJ5U7WfbYuXRrhhhuKdbZsO4nPFyUY\n3MP09AiQ6Oh+NPWdkgO88No+pExw9GiYeCiO5m++bP1aN2SrfifKFIMwNl5/E0+Q9Bjd8F2sZ6Ot\nThKxVqLXErW2DGwhFsxzJe0Df4apSwFkKAVIBoMmA7H6JqpGKIqCP+AH4eJed4anj23l5L/9cp0j\nI2Ssn8EcmkWEs4R0g8H+wZautZIvbfkNydvrjlkoxJcIEV1vzpO+3puE1fJ/jE3YHH1Bq2yoygTD\nknvekVvy3cr3oZ2M+GbmUs+X2Y2Ckp4g8fBok8H+QW489NMcffkp5uMmUrcQQrBlMMjBfW8iHAov\ne369xWtb7E6i+4+hBh3c7VMs2PWj3oTqoqiwfWKCf/LOd9Mfa5wvUY9mfGmtbEjS6eNkswLXzaOq\n5eTZZ4hG3wCMrHj+em8SVktYPfCRZEtjr1WduEY5M+3iCRIPjw7YMv5OXscsl+ZznJ25zJ7xUQb7\nDPrGfmHFcxsvGuNMTv0L/se3vkwumwXg+e+8g0z8qtYhFMG24W1sl30M9LXeH77bvrT5+UcQ4hdr\nysZD0czVjCDxWFvqa2BeZrvHOhDsm2fm/AESge6r4RsFI3wD/ePvRQt8m9GhKKp/rCul83eM7+B3\n3/uvK3//nZcGGL+zezv2bvvSyv1SqhHCKBWG9OgG3TT/1Tv+a1/0Mts91oEbf+Zr/Iv3jLFtZFvX\nxlzuZekm3XwpjfANXS2Tb6ZPkJ7/Nk5+uiKY4M6ujV+mm7605fulrK7z+DN/NMFTf1fbjyQe96H2\nX+Smt//3hue9864tXJpeajrcOubwv37Ye13C19v8txzrPwMPjyqWe1kmX1VLhfNq2TrW+kJV7zqP\nf9/g6R/pSwTMWkaFmekTxKceRvFFUfWRSk8Xp3AQWN7nsp50o19Ku1y6YBDqr+1HYts6C6nlgx0u\nTatLMs6BJY5xj5XxBMkq0UzjqF5go8wTYMcuhzt/qn6IZjdIJRRCYblEwKzlji89/+2iECk5wct/\n2uYFYN+azaNVOjWVdStq6uLFXczN6eTzCqbj4+m/eB/xI3u44WBozUPE1zsSbS1ZV0EihPh54E8A\nFfiMlPJji/5dlP79XiALvFtK+fyaT7RFutE4ai3YKPNsl7Uyk3UTJz+Nqtc6pxU1gutk12lGzdOJ\nqaxbC2uhECASSeO6Ko6tEeqfZWR8O9PnYiuf3GV62RTVbdbtGwkhVOBPgZ8FpoBnhBDflFIerzrs\nbcCe0n9vBD5V+rOn6bRxVCO6rT2s1jx7hW6+yGu1u1T9Y7iFREUTgWJPl7HtZlfzHFZ6ljaSpuqx\n/qynaLwNOC2lfBVACPFl4B1AtSB5B/BFWWzC8aQQok8IMSqlnFn76TZPJ42jGrEa2sNqzLNXWVyk\nDorlXMp1wVZirXaX4cG3EZ96GChrIilcO8m/+qjACHcnyXOlZ2mza6rrRfkZvFoksUizm5Ferni9\nnoJkG1BdsW2KpdpGvWO2AUsEiRDifuB+gP+/vXOPkrMsE/zvqXvfO+nOPemEEEQgyEXkJgisDiC6\nA+iAjOONI8u6M15mXPYMe5whh1ldXc1xVlf3SNbV0WHVZbxkQC4ZcONBZBTFBOiQEAgkne5O0ul0\n+lJV3XV994+6dFV1VXddvqrvq+7ndw6kuuqr73vqra/e532fa1+fdVFE1VCPwon12D00ssBjucz3\nYym2IyiXwiJ1HV1JpiZceX21nVCewt9+Dt3r786L2upcfYelUWEL3UvNtlNdtS7C/kMrSEbcxGI+\npqdbiEZdeBYoj7JqbaKoY72a4I1S5C5ghgbc+HwQjcLAG+7sAqbcxYiT/SqLxlhnjNkB7AC45JIL\nzAKH15V6FE6sx+6hkQUey2W+H8v2bZ2Wrchyf8SZci7zlaeoRYlVSjnhxMVChMtVNgvdS822U73r\nPw4wveJbREdbeOHhP2fLluOMjPiY8aVK1O/9zTImTvnydgEAV1wbqfvknLuAme31LouusKmdimQI\n2JDz9/r0c5Ue4zjqUTixHrsHuws8Vkq9f/Tznb9wErKTwhDhb26/lmNDXnytLuJmPf0H7yYegQ4T\n5x3veGTO+xe6l5y2Uy3fP2VoaxtnbKyHYNBD1BPFFY0THfOzYVOi4dF4uX3egWyvd1+g9nWu0yLC\n7FQkvwPOEpEzSCmHO4APFhzzMPDJtP/kMmDC6f6RDFYXTqxk91COo7TwmHXr7nasAqmWehaps5PC\nEOETx9ewbsMxxPUSEriIwydHiU5DeHhT0fcvdC85bae6kH+qva0dj8dNtG2KM6/+Ea1TnYRbp5De\n0wRafUy+eC5venPV1T+qprDnzK6dLdndSWG5+kpxWkSYbYrEGBMXkU8Cu0iF/37HGLNPRD6Rfv1b\nwGOkQn9fIxX+e2c5556ZOcqRI9sXVaRJubuHchyl1TpTt21rZ/fu9zM8DPHANG5/gskXzmTL2T5H\n2m+tLFJXi6PT6tVjsRBhER/J+BTlpNItdC9VslN1QnRXz7Ie7v7Av+MffvJ9xuU04a5xxG3o7Ozg\nw7d+iAcOtgL2O6QXM7b6SIwxj5FSFrnPfSvnsQH+otLzinhtjzSpxw+snF1OOY7Sap2pAwNuenvH\nmZw0RNuCeFoSrNkQYXhg/m6AlVKPbXutES/lXLeU3P17vdxY0PEOql89FgsRNiaKy9NR9jlK3Uvb\ntrUzMOAGrsCYyxkfH2N4+BgdHad45zu/mHdsa+tJ1q3bQzzuJ5Hw4XYfwON5lKGhiwiHV7BqVSfv\ne98dLKuwxH01bN64mb/91Of4+S8e5V/3/Ia3br2YW2+4BZ+v8TuRUmQaZ40cdzEddvHTB1O/m9Y2\nw2fvXN7UiYqLxtmej9gaaWJn+GQ5jlKnO1PrsW234ge6kIIrJXexsi61UBgibJJRTDKCv30rsRqt\ndgMDbjZtShCJRHj55Zd448gMMU+MkeHldA2G8o697LwDDI+7iMRcpFb8LvxeFxHvAX4/2IocirNn\n7w5uv+1SrrrqOlyu+jqYvV4vt954Czdf/8d1v1a55C5g+s5IAAlCz/g48+zonLDzZk5UbF7Jy8Cu\nydHO8MlyHKVOc6baRaU7H6fYpQtDhMXlI9D5Vty+XmIzMzWfP5lM8uKLv+PIEQ+JrhB44ki8HffK\n8bzjunrGmIq0IL7Za0YxdHWM4Vo5jkkaho6v4Ic//C2JRJTrrnt3zbKVQ6ESsTP/InMfFd5rUxMu\ndu1sKTuPyeksakVi1+Ro54q/HEep05ypdmGXYqg2MS1/Mno7mYrAx0fc+NsT9O/1EpwMcHrySkwC\nvBi+//0/5dixdu6/v/zCiclkkmg0RtJ4EVcSf8DPGSvP5a/u+su84xJj38EkphD3rEkt83fL+qt5\n/JdPEAvEiYY6OHWqdDXdJx68gn/9p7lRcZnxsMosWTiZDw94GmZSyr3X9u311ux0r3VMii+iztxU\nlTAsWkViiMcnbJsc7Vzxl+MorWfYr9PCEsshN2kMUhFe11+0CgxsvSiWff65Z3x5iWTVMptb4MpT\nZAspsFKKD+Cr3x3js3cup3d1kF8//yzRafBMdNHTE0v7PKpHRGhva2fj+o15z0e670iHIedm4Ru6\n199BfMQNImWd//TJTs67qrRCt+q+ccqO0gpqHZPiYxGJVnu+5hvBMjAmhtfbZVtOREvL2YyO/j3J\nZByvtwefbw0ul7thSq0cp3w14cl9fQl27+4mGIR4PIB7JsGxo362nD17QzbjjzU/6x0yYcIIeZ9l\n315vVYlkrW0m7/NnQpGbPQx53iz8kYN5x7rdxzhyZDszM4MEgz46OqZhtM8myUvTjAshJ+DcX3cN\nBAIb2LjxHluuHQy+zNjYv9DS8mai0WPEYqeIxyfo6/urpg9Fvv/+IGee+ROeeipJaOVxAj1RPnPn\npy1tbAXOrimUS6GJqn+vl+ee8dHaZvJ2MtffPF3XftlW0teX4I03PJw+3UMoFAC3m1jEw9pLi8vr\nbz+H//GVy+ZMvhNT5zA4dhtnb32c7u4RWlv7icUuxudbSzL5Blu27GNovIVndt/OyVfXEhptyXt/\nR1cy7ZxuLI1YCGWityC1qMic22n3dyUsSkViJ7mO9tbWzQDE4xNMT78C/Ft7hWsSnLry6+hK5tXm\nOj7kpq3dsHpdKms6MwHlll1pNu6/P0gkEuFrX/sHnvv9CjjrNbpXtnHPZ+4v+Z5ik2/L6RkOvtEL\nwMaNr2JMS9bU63K1E4v52bLxVZ78ZS/+QKxgR5jxHdhb96xe5JpGm/leyUUVSQWUkxvi9NBaZZbM\nzic36x3IFnQs5MrrInk//MKdxbO7/Rx6xct0WHjumdmEwdY2M2dXspRoa5vEmPwEyljMR0f7lE0S\nzU+hzwxSO4ft2zobnsfULKY2VSRlUm5uiIbWNg+ZH2IxU9OunS3F3jIvUxMuBGhtNXktXCfHXXmT\nQbWTykLvW9sX543XvARP9xKPgDvUzqlTcS6+2NqVfeHk9twzvnlL8odCnYjkhyV7vVFOj6eivVra\nInOil0JBsXySLXfcMz6zo4c9RGdSAQPRKOz8QSvDA56qJvFqJ/16mdqKj4W/6oQnVSRlUm5uyFIP\nrW0W/0YuxWQ26f8VqwRcKwtNKvNNkPOZQe65f5LTE6f58gM7CI2Bf2Aj73hHmE984q9rljmXwskt\nE85aKpT1yJGzOPvsfuLxCdzuDpLJIF5vhNeOXATAlrcMcu5ZnXnvGTzsWXCcKp1kK53MozOSrtYL\nINk2zE4OHCmXYmPx4+8fOlzt+Zp/RBpEuSarZquoazVO2m6Xi9NkdnLk2/ZtndkdSIahATcT4y66\nuotHoY2PryQcfgder5uZmUFcrg5ee+0CTp1e0Sixyya3Ym+mWi9gScXexYz9d2aTUInJyurKv4oz\nKdzJhIJCNArtnYt30hke8NDWbvKc4xPjLoKTgsczG4E0MRWgtWs0e0wisYaNGz8CwIEDLzE19VMA\nWrpGGTtxDoNe63d+1ZBb6uYfv9WefT46IwwNuNm1swWzeL/eqlFFUiZL3WSlzKVwJ/PZO5fnZS07\nAb//JEeObOcrX7mCEyf68PvX4/V2Z1/v60vMyXoXEVwuAQPGGCYngnz+m18A4IUDH+XU6HpOh3KK\nUHrB09qCv3eQ5Zd8D4DWmRnap4IkR7uBJG538aTI8657iLe/7XJuu+k2az+4BcSi0JG3KBA6u5N5\nXRWbxRleb1SRlEm9TFZOKMOt1M72bZ307/EyfNTNkddnf1Yej2HthkRDV9gvPPl+Jk700BJzMzIy\nws9/vpLnn38Tq1aNcemlv6Kz8234fCmz0uHDcyd4n8/H1Ve/i6NHf8XgyAriy04zEjkFwEx4hngs\njrhjee+Jx7zMhGcYGT6Vfc7EvLgnOjjrvDhXXvlv6viJS1PNRJ/ZacbjQiQnRmBmBg4d8BIOC9df\nuIpwSBgdcdPSmmTl6tTiIRNwYJUZsll8jqpIKsBqk5WdVYKbESev/oYHPNx46zSJ6CjR8Ksk41O4\nPB2MnDyfrz3Y2B1KaLyH1q5RNrSOs2xZlDVr2jhwIEow2I7LFSAcfjWrSEpx2WVXs2XLm3nwwX/k\nwCtC8ETKJ+IJteEnQXSqO+/4RMxHJ1Faj86aetva4rz3A2/ihhveg9dbWwXkYt99/x4v/Xu9bL0w\nX6nlTrLV+Jsy91L/3lXkFnkZSpeaaWk1iMCa9QnCQReZnQrU3rCqlCxORxWJjdhZJbgZscMJXYny\nSkRHmZl8HnH5cbnbMckI0fBBIsFY2f3UM+e2YhXq90cwJv88In7i8fImp5MnT3DyZITxoI+MKkwA\nvasPzTk2FFzGORfsIpo79U57GBwcJBQK0d1dmyIp9t1nIqgqSejL5IgMDbi56qz8XJ+tF8Xyvtet\nF8byrpnpcGi1slgMqCKxEU1edD6VKK9o+FXE5UdcfiA1aYt4CZ56uCJFYtUqNBLx09qaX4fPmAge\nT2eJd2SOMTz66A+4776tnJq+HfFFM8FLjAdXMh5cQdfqw3nvaVl7hPjGgbznYknhyV+v4/Dhb3DX\nXbexZcuba/5MtZLJERkacM/J9Vksob12oKNmI5q8WH8aaQ5LxqdwudvznhPxkpgZtvQ65TI+3ktv\n7xtMT79BPL4BERfx+ATd3efP+75oNMrLL7/C2OnraNtwCK/fxcqelakX33yU0yNd3Pap3xR551uz\nj0ZPj3JkcIDEsilGRpaxd+9ztimS3Ez1oQE3J4+7CYeEo4c9bJin5lmxqLxMsc1qincuZlSR2IhG\ngtWf3B1F7oTy3DO+rILJVSqVZm3n4vJ0YJIRRPzZ54yJ4Q6snedd9SYVddTeHuTkyVUMDa1kYiKA\n15uy9/f1zZ/1LoDH4+Et57wl+9xgi4cP3fqhed938PWDPPDD/0UCQcosJ28Vme+wf6+Xgdc9RCOC\ny2VwuyGRgGQSXC6yWeulKBaVl7mXMpUPfAFDcFLmFGF0mjO83qgisZGlnrzYaPLLxbvyiixmqDRr\nO8PavjhHD51PNHwQES8iXoyJsWr1Mdp7ZjsDNmKH1NZ9iokTvbQHghw7tplodA2bN8NVVx3gU5/6\nMV7vk7ZVx24Eme9w314v3cuTjJ5wZZUIQDQCJaKR5yV3h2IMHBt0p6Ly+pKcl3b4OyHwww5UkdhM\nsyUvGhuzsewIhRw5njKFRKP59bdGjrnzuhsCuL3dbHpTH3f9h29k+3O097w7zz/SiICBC/7oJ4TG\n4IbNL7F8eYBLLrk6+5oxpX1w27a1MzDgJpFIsH//xzg1upGJ6eW0dMzA5bXJZMzs+cfHz+XVV9sJ\nRjzICyGmXvFz203zv7/a737DpjjRGS/+gCGywA5kIZaigiiXJa9INI8jn4MH9/Pww48wPR0r+vrx\nE0IoEINACJfLj9/nL3pcPWjUDznXBDY1kZp8ksmUUlm5OrWsjcXym15lwn4PHwwA0LXu43kKJLMT\nKSwvUmgyq3XH4vP6cLld4A1zOuqBsQhPP/109nWPJ0I87uehh74w572PPPJndHePYQwkki48gRDe\nlhCxmfmd88UI+AO4RKBjkvFQgGd+PcCvnu6nu3uMeNyAx01rVwjxJZgau2DB81n13WdMUV6fYTrs\nyiYXZpqPWbkocXK4utUsaUWieRyzhMMhfvazH/DkL8aY9Icw7uI/KAnMIMsj+P0B3nfjrfQu722w\npPUn1wTW2WWyq9kVqxPccEsqo/unD7Zmj88L+3UtIxmbYHxwB93r784qk1xzS27me6HJrJIdS/GJ\najnetvto6/s7DoW6uXjZ64xNtxCJ+fB7owRMlN8fPINTE6E55zs1HWfam1pASGsUb9skyVgnLd5e\nBg/PylzOZLth7Qbe+8738F/vbSF4ejnPx92MDp6Bx7sWEfC2TrFi2T56e3voDawGGjOxbtgUZ3Lc\nxQ23TNe9F4iTa6ZZzeL7RBWgeRyzPPTQt3nqqQThlaNIexi3u7hPQBA2n7mFj936UTraOhosZeXk\nmkRy+45Y2eY2L+xXBHf6PgqeeryisN9KKT1RreELn/4b/unRH/PCYQ+be4/S1T7OVKSVAyfPYMLn\nxbNibi8QVyCGqzW1OxJxcdk102zZtI7hgWTFE66IcM1l1/DQ2jZOdD7H1OQUE6c24A2EAUhEO3jr\n+RezomdF3SdWX3ohEI2mFPdSdYjXkyWtSDSPY5apqSni8W7En6C9s5WPvf9jtLW0zTnO5/XRu7y3\n4ZE41ZJrQihcwRdrcXrk9VSJE48n5QsKh1J1p+az4CXjUzz/u4sITrUQDvv42//0p2AMyeQ0Z55f\nfTOkWmgJtPCR93+YU6dvYianzsc16X93fGU9J4byP1RiooNAS5S3vn0Sn9eH31+72dLv83PlRVcQ\nCofYPdpFR2fKNDgd8rOiZ4Znd/s5PjTX31SL+SezeDDMOsSD0dT9GgoKrW2p73Z4wFNTsyplliWt\nSDSPoxTCulXraGudq0iamXImjI2bEwQn41nz06EDCztqXZ4OpiZ9dHalzF5r14+lwoBd/uxklfGN\njBx3Z0tteH2GtvaUbf7I66nJdCEfSqX0LOsp+vzU2DLedE7+ivyNV1xEpgN0tFscUCHQ1taG1+PF\n50t99plwajynJlzZPh+51LJLKfY9F2teVsl1lpK/oxqWtCLRPA5lIXLNIhmTCMCqtYns40TsfMLh\ntMmscxqTjGCSEfztW2E8v/R6oX/kvAtjfPW7Y9mJbiEfSj0p7EmfoVYTUCZ4IVeJxuPCEztbCAeF\n1euc35t9Kfk7qmFJj4LmcSjF6OiazRvJNGsKBYVbPhguufr8zIdg5YqXScanEFcH/vatuH29Rc+Z\noVg72cLjGmnPL+xJbxWZ4IViShQoOkEvBpqlcq8VLGlFAs2Xx6HUn2KmpIVav7q93bR0X1HzOQuP\nm29ib4aJKrfjYC5WBjs4laVk8lryikRRSpGbTxIKStYh3Ci7+EKO6HrKYJVPILfjYCFqFpqfZvLL\n6DepKOTUZ9rjTa+gyWtatHpdomhJlQzz7Q6KTQblUMoR/cTOlponmIV2M+VO/rVOdgvJUen5Sx1/\n5PXiNVHquXurdWyaSQE7TyJFsYHMj7aw/wSQTUKcj/kmhu3bOhc0QRWbUEMlHNHhoNQ8wVi1oq11\nsltIjkrPX+z4Z3f7OTHsZuPm/LG0UvGWKws4UxHUyuL7RIriMMqZrCoJWa0EJ5hH7PblWBFi7DRT\nktNQRaIoDcKOSd0Jq+J77p/M++z9e7yEQwL4+JeHW7Ktcp1o+1fKwxZFIiLLgf8LbAIOA7cbY04X\nOe4wMEWqw2fcGHNJ46RUFGtxwqRuF7mffd9eb7Y7YaYzISyNcVis2PXN3Qv8whjzJRG5N/33X5c4\n9jpjzGjjRFOUFIUJev17vYTTJTZyI6nqtZIuZRLKlPgoRe7qPzdTvpIsebvNURn693jzMv0zZLoZ\nvP+aFZwYnnWkj46kSqK0dxhuv3NuYcpmwinfQTnYpUhuBq5NP/4e8EtKKxJFqTvFfrR9ZyS4/JpI\nVknUWmajUgqVU1ZBSKr68PiYi3hc8HgM3cuTWQXXv8fLjbemAgRyM+UziY7l1LeqpxO6IkpVpkk/\nf2I4v/d6PC5EZ4Tx0668Yp12ZM/XOjbNZOazS5GsMsYcSz8+DqwqcZwBnhKRBPCAMWZHqROKyN3A\n3QB9feuslFVZAjTDj7YwsmzXzpZs98bcyLJM+HIprKxvVe9x23phrGgUVkYRjo64CQdTCtIXMNke\n7McG3dlEzowCtrr0y0I0wz1lFXVTJCLyFLC6yEufy/3DGGNEpNRe/SpjzJCIrASeFJEDxpinix2Y\nVjI7AC655AL72vgpTU8pp3j/Xm9dy3nUwxmfW3IlU27FrhV6LkcPe4ima5hlwqxDQSmrGm+uIvR4\nUv1igJKFNZfShG4XdVMkxph3lXpNRE6IyBpjzDERWQOMlDjHUPrfERH5GXApUFSRKIpVlHKKL7TS\nXwirkgArIdcnkim3YkVYcaXkfvaRY27Gx1yIy+BykS3k2N5hFkzefHa3P3v8rp0thEMuYlGD2wOB\ngK4f7cIu09bDwEeBL6X//efCA0SkDXAZY6bSj68H/q6hUipNhZ05E+VceymvjHM/+2fvXD6nyjGU\nV+l4asKFL63PO7uTuFwGjwfizvM/LynsUiRfAh4SkY8DR4DbAURkLfBtY8xNpPwmP0s3UPIAPzDG\nPGGTvEoTYNWKvlSkUGjKVXJHUe21rYiwKqQ13eOkmJzNxPZtnfTv9ebtBEdH3MRj0LsqpYR8N8A8\nsQAADIJJREFUfohEIJkUwmGyPdhXrXV+afrFhC2KxBhzCnhnkeeHgZvSj18HLmiwaFURDL6cV4q+\np+empqsobIyaBTKEQ5IXCZQhFJSSlXgLI6DKYfu2Tnb+oJW2dCOp8TEX4WDKaVyMQtNYpgNga1u+\n4rj+j6fn3f00Oqy0cLf23DM+xsdcTIy7ss7xYgwPeLixoDzNrp0tDA24s+8778IokN/bRWk8izID\naGjoGPfd94WGXKutbZSNG/cQj/uJx314PPvxeH7OkSMXEQr1LnwCR2AYPu4l2nMc8UbxePx4PXNX\n5Iq15Da8Ajh53D1vN8ZaTGN2mv0Kd2u7nwgwMy3MTAvRmdn7zGA4r4zzeX2mrN4uSuNYlIpkOpGk\nfzzYkGtdtmY/J0JCJCZADBD8XoHO/fQPXdQQGSxheRjxxenu7uZj7/sIPl9tjuVmprV97kSVeb6e\nlOrGaMUEaaUjv1alFIumTFLRAsvddNhV1mdduTo5p5DmQv1ilPqyKBWJeJJ4ehqT1dq1bIJgpAWX\nJ5Z9LobQ1TbRMBmsQFwurn7btbz3ne/B613au5FiuQtQ/xIeGXNNJWaaZqzf5fWBkIrYWrF61oRo\nzMK7rnq1A1ZqY1EqkjUrV/Of/6IxifKRkQcwiUnE3Zl9LvP3Ze/69w2RwQoCgQCd7Z0LH+hg7Cwp\nYde1G1G/q5iPY99eb9UBAStXJ4omUpYjc73aASu1sSgVicfjYWXvyoZcKxL4E8YHd+DyxHG5O0gm\npkjG43Sv/xP87Y2RQUlh1Qq8GqVgRcJgBqfZ+wuVVSZ0t5xw3VpoplpTS51FqUgaib/9HLrX303w\n1OMkZoZxB9bSufoO/O3n2C3aksFq804j+3SkqL7hUjNQqBBCQQFcC/ZtX0xjsNhRRWIB/vZzVHHY\nSLOWZ691oty+rTMv9yRDR1eSvjPmKicrV/dHD3sITkq2vAmULnFSsvgks99R/14vmLlh1ItNqS5W\nnP1LU5Qmpt6O8MLw4Qwpk1O+Iqn2esWU1chxF1MTLgIt+VFsq9clyupPX0k3SKcuBpzQedJJOPNb\nUpRFQCN2SvX2sRRTVp3dSfa/6GVdX6JoGO5SoFl3wfViaX5qRVkkFIuasiqnIrMbCQUlWygRUuG7\n8bgs6ONQlg6qSBRFKUpmN1JYLmZy3EUoKFx5XYRnd/uZmpjdEYWCwmf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4877e5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train AdaBoost classifier on 200 decision stumps (DS)\n",
    "# DS = decision tree with max_depth=1\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "ada_clf = AdaBoostClassifier(\n",
    "        DecisionTreeClassifier(max_depth=1), n_estimators=200,\n",
    "        algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42\n",
    "    )\n",
    "ada_clf.fit(X_train, y_train)\n",
    "plot_decision_boundary(ada_clf, X, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting - Gradient Boosting\n",
    "* Similar to AdaBoost (continually correcting the predecessors in an ensemble. Instead of tweaking instance weights on each iteration, GB fits the predictor to the *residual errors* of the previous predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.75026781]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# training set: a noisy quadratic function\n",
    "rnd.seed(42)\n",
    "X = rnd.rand(100, 1) - 0.5\n",
    "y = 3*X[:, 0]**2 + 0.05 * rnd.randn(100)\n",
    "\n",
    "# train Regressor\n",
    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg1.fit(X, y)\n",
    "\n",
    "# now train 2nd Regressor using errors made by 1st one.\n",
    "y2 = y - tree_reg1.predict(X)\n",
    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg2.fit(X, y2)\n",
    "\n",
    "# now train 3rd Regressor using errors made by 2nd one.\n",
    "y3 = y2 - tree_reg2.predict(X)\n",
    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg3.fit(X, y3)\n",
    "\n",
    "X_new = np.array([[0.8]])\n",
    "\n",
    "# now have ensemble w/ three trees.\n",
    "y_pred = sum(tree.predict(X_new) for tree in (\n",
    "    tree_reg1, tree_reg2, tree_reg3))\n",
    "\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O86Ml1B7byhxnhLOs7+/1fX538O/CWViRzZ07t30AaKlc77zzDjvssANXXHEF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa48745cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(\n",
    "    regressors, X, y, axes, \n",
    "    label=None, \n",
    "    style=\"r-\", \n",
    "    data_style=\"b.\", \n",
    "    data_label=None):\n",
    "    \n",
    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
    "    \n",
    "    y_pred = sum(\n",
    "        regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
    "            \n",
    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
    "    if label or data_label:\n",
    "        plt.legend(loc=\"upper center\", fontsize=16)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(11,11))\n",
    "\n",
    "plt.subplot(321)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Residuals and tree predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(322)\n",
    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "plt.title(\"Ensemble predictions\", fontsize=16)\n",
    "\n",
    "plt.subplot(323)\n",
    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
    "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
    "\n",
    "plt.subplot(324)\n",
    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "plt.subplot(325)\n",
    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
    "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "\n",
    "plt.subplot(326)\n",
    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
    "plt.xlabel(\"$x_1$\", fontsize=16)\n",
    "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
    "\n",
    "#save_fig(\"gradient_boosting_plot\")\n",
    "plt.show()\n",
    "\n",
    "# 1st row: ensemble = only one tree: predictions match 1st tree.\n",
    "# 2nd row: new tree trained on residual errors of 1st tree.\n",
    "# 3rd row: \"                                              \"\n",
    "# result: ensemble predictions get better as trees are added."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *learning_rate* param controls contribution of each tree. Low values (ex: 0.1) = need more trees in ensemble to fit training set, but predictions usually generalize better. (This is called **shrinkage**.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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RkZFBixYtaNasGfv2uXf1voIC0wJSUODaJmNa0d5C+hR/Qb/yLziu+EsWLyyNdpGEEHEo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\n7LHH2Lp1K1lZWRx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1aKdFC3PG9vPP8OWXMV1zmbTJZTAdzqVpTwghPPmNnXbtybffkvft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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4877aa198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# two GBRT ensembles trained with low learning rate\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbrt = GradientBoostingRegressor(\n",
    "    max_depth=2, \n",
    "    n_estimators=3, \n",
    "    learning_rate=0.1, \n",
    "    random_state=42)\n",
    "\n",
    "gbrt.fit(X, y)\n",
    "\n",
    "gbrt_slow = GradientBoostingRegressor(\n",
    "    max_depth=2, \n",
    "    n_estimators=200, \n",
    "    learning_rate=0.1, \n",
    "    random_state=42)\n",
    "\n",
    "gbrt_slow.fit(X, y)\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(\n",
    "    [gbrt], X, y, \n",
    "    axes=[-0.5, 0.5, -0.1, 0.8], \n",
    "    label=\"Ensemble predictions\")\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(\n",
    "    [gbrt_slow], X, y, \n",
    "    axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
    "\n",
    "#save_fig(\"gbrt_learning_rate_plot\")\n",
    "plt.show()\n",
    "\n",
    "# left: not enough trees (underfits)\n",
    "# right: too many trees (overfits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To find optimal number of trees - use early stopping method.\n",
    "* *staged_predict* method: returns iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.05877146809545241,\n",
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       " 0.0026497886163651938,\n",
       " 0.0026430582537166087,\n",
       " 0.0026548742317473117,\n",
       " 0.002660275592603878,\n",
       " 0.0026582571161537366,\n",
       " 0.0026570823709750535,\n",
       " 0.0026557538081706522,\n",
       " 0.002675470519360824,\n",
       " 0.0026762761989050578,\n",
       " 0.0026742086578626454,\n",
       " 0.0026957941482744232,\n",
       " 0.0026964801899977998,\n",
       " 0.0026939578807501376,\n",
       " 0.0026959742963617757,\n",
       " 0.0026949319702616616,\n",
       " 0.0026988916344244736,\n",
       " 0.0027169473218451121,\n",
       " 0.0027148017926961689,\n",
       " 0.0027192710134859655,\n",
       " 0.0027358435370699618,\n",
       " 0.0027346474658663323,\n",
       " 0.0027351047440069571,\n",
       " 0.0027459941366245631,\n",
       " 0.0027441324932851491,\n",
       " 0.002756368378237764]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y)\n",
    "\n",
    "# train GRBR regressor with 120 trees\n",
    "\n",
    "gbrt = GradientBoostingRegressor(\n",
    "    max_depth=2, \n",
    "    n_estimators=120, \n",
    "    learning_rate=0.1, \n",
    "    random_state=42)\n",
    "\n",
    "gbrt.fit(X_train, y_train)\n",
    "\n",
    "# measure MSE validation error at each stage\n",
    "errors = [mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)]\n",
    "errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=2, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=79, presort='auto',\n",
       "             random_state=42, subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train another GBRT ensemble using optimal #trees\n",
    "\n",
    "best_n_estimators = np.argmin(errors)\n",
    "min_error = errors[best_n_estimators]\n",
    "\n",
    "gbrt_best = GradientBoostingRegressor(\n",
    "    max_depth=2, \n",
    "    n_estimators=best_n_estimators, \n",
    "    learning_rate=0.1, \n",
    "    random_state=42)\n",
    "\n",
    "gbrt_best.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WzZvhhhugsjLTJRGRfKRgMwXFxdCjh4JNEcktS5fCtdfC6NEKOEWk+SnYTJHSH4lIrnEO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\nTy2hF/DFrvZ0bsQpVZOdPCV1l7SJrq2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YNCjhW4cDlgoLNdOP1KWaTZEsoDybIpIWZjBn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\nXGyamZmZWcf8H7UidUQFsTGTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa485f98e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(errors, \"b.-\")\n",
    "plt.plot([best_n_estimators, best_n_estimators], [0, min_error], \"k--\")\n",
    "plt.plot([0, 120], [min_error, min_error], \"k--\")\n",
    "plt.plot(best_n_estimators, min_error, \"ko\")\n",
    "plt.text(best_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
    "plt.axis([0, 120, 0, 0.01])\n",
    "plt.xlabel(\"Number of trees\")\n",
    "plt.title(\"Validation error\", fontsize=14)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
    "plt.title(\"Best model (55 trees)\", fontsize=14)\n",
    "\n",
    "#save_fig(\"early_stopping_gbrt_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Another method: actually stopping training early\n",
    "* Implement via *warm_start=True* (tells Scikit to keep existing trees when fit() is called - allowing incremental training.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n"
     ]
    }
   ],
   "source": [
    "gbrt = GradientBoostingRegressor(\n",
    "    max_depth=2, \n",
    "    n_estimators=1, \n",
    "    learning_rate=0.1, \n",
    "    random_state=42, \n",
    "    warm_start=True)\n",
    "\n",
    "min_val_error = float(\"inf\")\n",
    "error_going_up = 0\n",
    "\n",
    "# 120 estimators.\n",
    "# stop training with validation error doesn't improve for\n",
    "# five consecutive iterations\n",
    "\n",
    "for n_estimators in range(1, 120):\n",
    "    gbrt.n_estimators = n_estimators\n",
    "    gbrt.fit(X_train, y_train)\n",
    "    y_pred = gbrt.predict(X_val)\n",
    "    val_error = mean_squared_error(y_val, y_pred)\n",
    "\n",
    "    if val_error < min_val_error:\n",
    "        min_val_error = val_error\n",
    "        error_going_up = 0\n",
    "    else:\n",
    "        error_going_up += 1\n",
    "        if error_going_up == 5:\n",
    "            break  # early stopping\n",
    "            \n",
    "print(gbrt.n_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking\n",
    "* Instead of using a voting function to aggregate an ensemble's predictor outputs, instead train a model to do the aggregation. (\"blending\".)\n",
    "* Blender training: common approach = use a *holdout set*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# todo: stacking implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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